Verification
step · paper openDeclaration 8f199958cdfb has not been accepted by anyone. · no paper read under it
Did anyone check, and what did they find?
Mechanical: re-runnable from its declared inputs.
Part of Measures — What can be measured over the tree - its coverage, its wording, whether its code re-runs?
How it works
Runs the paper's own deposited script under observation and records what it actually did — every path it opened, every value it computed — rather than what its output said it did. Nothing is taken from the script's own account of itself, because that is precisely what failed: the first script audited had a claim recorded verified while the function named in its record raised a shape-mismatch error, and a reproduction record naming a file the code never opened.
This is not replication. It re-runs the authors' code on the authors' data, so it can catch a record that does not match its own run and it cannot tell you whether the finding is true.
What it found · added 2026-09-06
133 of 151 claims that a re-run could settle carry a reproduction record, and their outcomes are not uniform. Observing the runs from outside also showed that most records were reached by reading a deposit rather than by running the paper's own script.
Rests on
- claim-tree · step
Feeds — a change here disturbs these
- verification-check · feature
How it is defined
What this layer reads besides its dependencies. Each is a declared input: its content is hashed into every run, so editing one makes those runs stale.
The declaration names this path and the repository does not have it. An input that does not exist hashes to nothing, so it cannot make a run stale — the layer is declared to depend on something it is not in fact tracking.
What it says it does
Run a verification script under observation, and record what it actually did.
Only one of the nine verification scripts was ever audited, and auditing it found two
failures that no amount of reading would have caught:
* a claim recorded `verified` while the function named in its record raised
`shapes (4,4) and (5,5) not aligned` -- the verdict had been narrated, not observed;
* a reproduction record naming `fMRI - Choices_singleTrialData.csv` while the code opened
`Behav - Choices_singleTrialData.csv`.
Both were found by making the script report every path it opened and every value it computed.
That was done for Gaedeke by editing the script to call `used()` at each open. Doing the same
to eight more scripts would mean eight sets of hand-edits, each an opportunity to annotate a
path the code does not take -- which is the very failure being audited.
So this observes from outside instead. It patches `open` and the common loaders, executes the
script in-process, and reads its `ROWS` list afterwards. Nothing is taken from the script's
own account of itself: the file list comes from the file system calls, the results come from
the list the printed table is built from, and an exception is recorded whether or not the
script caught it.
What it emits, beside the script it ran:
verification/<paper>/provenance.json files opened, results produced, exceptions raised
Usage:
python3 verification/audit_run.py <paper-slug> [-- script args]
python3 verification/audit_run.py --all
python3 verification/audit_run.py --all --timeout 1800
`--timeout` now defaults to 1800s rather than to no limit. The documented example used to say
900, which is shorter than the slowest verification script's own internal budget: Ejdrup's fast
mode runs two figure scripts and allows each 600s, so its worst case is 1200s before the clone
is counted, and an uncontended run measured 1064s. A run killed at 900s writes no results, and
`audit_verifications` then reports the paper as a failed run — which it was not. The script had
been working the whole time and the observer was giving up first.
A default that cannot accommodate the slowest thing it observes is a fault in the observer, and
one that reads as a fault in the observed, which is the worst way for it to be wrong.What it produces5 results
One paper, as the worked example — Bouyeure, v1. Read from verification/bouyeure-2026-fear-rsa/provenance.json · 5 KB. script verification/bouyeure-2026-fear-rsa/verify.pyobserved_by verification/audit_run.pyrecorded 2026-09-13T15:43:58+00:00exit SystemExit(0)
| # | claim | paper_value | reproduced_value | status | measured |
|---|---|---|---|---|---|
| 1 | cs-plus-univariate-fear-network-acquisition | dACC/SFG cluster confirmed, >100 total sig voxels | total=5544 voxels, dACC/SFG=1075, peak=[6.0, 15.0, 39.0] | PASS | true |
| 2 | cue-generalization-increases-acquisition | 2283 sig voxels, peak -log10(p)=2.959 in dACC/SFG | n_sig=2283, peak=[-11.5, 10.0, 44.0], peak_val=2.959 | PASS | true |
| 3 | current-threat-activates-fear-network-reversal | >1000 sig voxels in fear network | total=7473 voxels, peak=[-9.0, 10.0, 41.5] | PASS | true |
| 4 | prior-threat-activates-fear-network-weakly | ~36 sig voxels, occipital peak NOT fear network (MISMATCH documented) | n_sig=36, peak=[-9.0, -92.5, -6.0] | PASS | true |
| 5 | behavioral-learning-confirms-contingencies | CS++ > CS+- > CS-+ > CS--, p<0.0001 | Download failed | WARN | true |
Across the corpus
3 not run · 7 run not observed·a paper links to its own cell, where this layer's output for it is rendered
Inputs and outputs
- Reads, besides its dependencies
-
- verification/{paper}/verify.py · declared, and not in the repository — it hashes to nothing, so it cannot make a run stale
- verification/audit_run.py · 416 lines
- Produces
-
- verification/{paper}/provenance.json
One per paper — the table above links each one that exists.
- Views
-
- graph — on the paper page, as the claim graph
- table — rendered above, over the 5 results in the artifact
- comparison — on the cell page, two versions aligned by the matcher, wherever the ledger holds more than one
Running it
The command comes from the declaration, so this text and what actually runs cannot
diverge. pipeline.py run also runs the unmet dependencies first.
python3 scripts/pipeline.py run <paper> verification
Underneath, that runs python3 verification/audit_run.py {paper}.